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Optimization of Oracle Real Application Clusters for High-Availability and Fault-Tolerant Healthcare Database Infrastructure
Subject area: Science,Engineering and Technology · Area of research: Information Systems
DOI: 10.64388/ire1724849
Abstract
Healthcare database infrastructure must remain responsive during emergencies, routine clinical work, planned maintenance and equipment failures. Oracle Real Application Clusters (RAC) offers a shared-database architecture in which multiple instances can support services, but high availability cannot be established by clustering alone. This integrative review examines how RAC configuration, database service design, connection recovery, transaction integrity, workload management, storage resilience, monitoring and disaster recovery can be combined for fault-tolerant healthcare information systems. It proposes a layered optimisation framework connecting clinical service criticality to recovery objectives, technical controls, failure testing and operational governance. Particular attention is given to electronic health record transactions, medication administration, laboratory reporting, admission workflows and interfaces whose behaviour during partial failures may differ. The synthesis identifies the most consequential trade-offs: local continuity versus site-wide recovery, throughput versus tail latency, automated replay versus clinical transaction safety, and infrastructure redundancy versus common-cause failure. A phased assurance model is offered for healthcare organisations in Saudi Arabia and comparable settings. The study is conceptual, reports no experimental performance gains and recommends prospective workload-specific validation before operational deployment.
Keywords
Oracle Real Application Clusters; healthcare databases; high availability; fault tolerance; Application Continuity; transaction integrity; workload management; disaster recovery; database resilience
References
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How to cite this paper
@article{1724849,
author = {Mujeeb Pullumalayil},
title = {Optimization of Oracle Real Application Clusters for High-Availability and Fault-Tolerant Healthcare Database Infrastructure},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {5},
pages = {1319-1330},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724849.pdf},
abstract = {Healthcare database infrastructure must remain responsive during emergencies, routine clinical work, planned maintenance and equipment failures. Oracle Real Application Clusters (RAC) offers a shared-database architecture in which multiple instances can support services, but high availability cannot be established by clustering alone. This integrative review examines how RAC configuration, database service design, connection recovery, transaction integrity, workload management, storage resilience, monitoring and disaster recovery can be combined for fault-tolerant healthcare information systems. It proposes a layered optimisation framework connecting clinical service criticality to recovery objectives, technical controls, failure testing and operational governance. Particular attention is given to electronic health record transactions, medication administration, laboratory reporting, admission workflows and interfaces whose behaviour during partial failures may differ. The synthesis identifies the most consequential trade-offs: local continuity versus site-wide recovery, throughput versus tail latency, automated replay versus clinical transaction safety, and infrastructure redundancy versus common-cause failure. A phased assurance model is offered for healthcare organisations in Saudi Arabia and comparable settings. The study is conceptual, reports no experimental performance gains and recommends prospective workload-specific validation before operational deployment.},
keywords = {Oracle Real Application Clusters; healthcare databases; high availability; fault tolerance; Application Continuity; transaction integrity; workload management; disaster recovery; database resilience},
month = {November},
doi = {https://doi.org/10.64388/ire1724849}
}